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gguichard/matching-rh-peft3

sourceHugging Faceupdated 1y agoView on Hugging Face
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SentenceTransformer based on EuroBERT/EuroBERT-210m

This is a sentence-transformers model finetuned from EuroBERT/EuroBERT-210m on the matching_rh_train10 dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

  • —Model Type: Sentence Transformer
  • —Base model: EuroBERT/EuroBERT-210m <!-- at revision 25797134448b4c66f39791922c88150a83e3052d -->
  • —Maximum Sequence Length: 8192 tokens
  • —Output Dimensionality: 768 dimensions
  • —Similarity Function: Cosine Similarity
  • —Training Dataset:
  • —matching_rh_train10 <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False, 'architecture': 'EuroBertModel'})
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

bash
pip install -U sentence-transformers

Then you can load this model and run inference.

python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("gguichard/matching-rh-peft3")
# Run inference
sentences = [
    '{"type": "opportunity", "customer_code": "", "opportunity_title": ".NET Developer", "opportunity_place": "", "opportunity_expertise_area": "Autres", "opportunity_tools": "", "opportunity_activity_area": "", "opportunity_type": "1", "opportunity_description": ".NET\\nReact", "opportunity_criteria": "", "opportunity_extract": 1}',
    '{"type": "candidate", "customer_code": "", "title": "Agile Back end  Developer", "skills": "", "education": "", "experience": "-1", "tools": "", "languages": "", "mobility": "", "expertise_area": "", "activity_area": "", "list_diplomes": "", "typeOf": "0", "source": "", "informationComments": "", "extract": 1, "experiences": "[]"}',
    '{"type": "candidate", "customer_code": "", "title": "Consultant Data", "skills": "", "education": "", "experience": "-1", "tools": "", "languages": "", "mobility": "mondeeuropefrancerhonealpes", "expertise_area": "", "activity_area": "", "list_diplomes": "", "typeOf": "-1", "source": "3", "informationComments": "pas à l\'écoute", "extract": 1, "experiences": "[]"}',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.8869, 0.1913],
#         [0.8869, 1.0000, 0.2530],
#         [0.1913, 0.2530, 1.0000]])

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Training Details

Training Dataset

matchingrhtrain10
  • —Dataset: matching_rh_train10 at 601ef4d
  • —Size: 297,400 training samples
  • —Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code>
  • —Approximate statistics based on the first 1000 samples: | | label | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------|:--------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------| | type | float | string | string | | details | <ul><li>min: 0.0</li><li>mean: 0.81</li><li>max: 1.0</li></ul> | <ul><li>min: 82 tokens</li><li>mean: 326.44 tokens</li><li>max: 1277 tokens</li></ul> | <ul><li>min: 95 tokens</li><li>mean: 1200.82 tokens</li><li>max: 6900 tokens</li></ul> |
  • —Samples: | label | sentence1 | sentence2 | |:-----------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>1.0</code> | <code>{"type": "opportunity", "customercode": "", "opportunitytitle": "SIENNA - DEV DOT NET", "opportunityplace": "", "opportunityexpertisearea": "Banque", "opportunitytools": "", "opportunityactivityarea": "", "opportunitytype": "1", "opportunitydescription": "", "opportunitycriteria": "", "opportunityextract": 1}</code> | <code>{"type": "candidate", "customercode": "", "title": "Consultant Sénior Microsoft .NET", "skills": "", "education": "", "experience": "-1", "tools": "", "languages": "", "mobility": "", "expertisearea": "", "activityarea": "", "listdiplomes": "2007 - Master Management des projets informatiques et systèmes d'information, 2004 - Filière Informatique et Réseaux - ENSICAEN", "typeOf": "1", "source": "1", "informationComments": "", "extract": 1, "experiences": "[{'skills': '', 'startMonth': '6', 'endDate': '', 'startYear': '2004', 'description': 'AUTRES MISSIONS\\nA\\nIngénieur Conception et développement CALCIA\\nAnalyste - Responsable d’applications chez EDF\\nIngénieur Conception et Développement chez EDF\\nIngénieur Conception et Développement chez BNPPARIBAS', 'company': 'AUTRES MISSIONS', 'location': '', 'id': '2536', 'title': 'Ingénieur Conception et développement', 'endMonth': '11', 'endYear': '2008', 'startDate': ''}, {'skills': '.net, .net 2.0, asp.net, c#, front office, gamaweb...</code> | | <code>1.0</code> | <code>{"type": "opportunity", "customercode": "", "opportunitytitle": "Consultant Mainframe - DGFIP - ONEPOINT", "opportunityplace": "", "opportunityexpertisearea": "Autres", "opportunitytools": "", "opportunityactivityarea": "", "opportunitytype": "1", "opportunitydescription": "", "opportunitycriteria": "", "opportunityextract": 1}</code> | <code>{"type": "candidate", "customercode": "", "title": "Ingénieur de développement\nPACBASE/COBOL/MAINFRAME\n2 ans et ½ d’expérience", "skills": "", "education": "", "experience": "-1", "tools": "", "languages": "français, anglais", "mobility": "mondeeuropefranceiledefranceparis, mondeeuropefranceiledefranceseineetmarne, mondeeuropefranceiledefranceyvelines, mondeeuropefranceiledefranceessone, mondeeuropefranceiledefrancehautsdeseine92, mondeeuropefranceiledefranceseinesaintdenis, mondeeuropefranceiledefrancevaldemarne, mondeeuropefranceiledefrancevaloise", "expertisearea": "", "activityarea": "", "listdiplomes": "2018 - Formation PACBASE - Banque Populaire Dijon, 2018 - Formation Cobol en alternance appliqué au contexte Descours & Cabaud - Alteca Lyon et Informatique, 2018 - Formation interne VBA EXCEL, 2018 - Formation Mainframe IBM/COBOL et Qualification logiciel - INTI Formation, 2016 - Master international Science de la matière - Université de Rouen", "typeOf": "1", "source": "3",...</code> | | <code>1.0</code> | <code>{"type": "opportunity", "customercode": "", "opportunitytitle": "STIME responsable application adjoint", "opportunityplace": "", "opportunityexpertisearea": "Grande distribution", "opportunitytools": "", "opportunityactivityarea": "", "opportunitytype": "1", "opportunitydescription": "", "opportunitycriteria": "", "opportunityextract": 1}</code> | <code>{"type": "candidate", "customercode": "", "title": "Consultant AMOA- Chef de projet SI", "skills": "", "education": "", "experience": "-1", "tools": "", "languages": "anglais, espagnol", "mobility": "", "expertisearea": "", "activityarea": "", "listdiplomes": "2020 - CERTYOU Paris, 2019 - Certification SCRUM Master - Actinuum Paris, 2017 - Cycle Project Management Professional V5 PMP, 2015 - Urbanisation et architecture SI, 2014 - ITIL Fondation", "typeOf": "1", "source": "1", "informationComments": "", "extract": 1, "experiences": "[{'skills': 'crm, oracle parties, mep, dba, infrastructure, crm people soft, uml, power amc, sql query, oracle, hp quality', 'startMonth': '4', 'endDate': '', 'startYear': '2007', 'description': 'INWI\\nà\\nSynthèse :\\nParticipation à la mise en place du CRM pepoleSoft Oracle parties : vue 360°\\nclient , facture et réclamations.\\nRôle :\\nConsultant AMOA homologation\\nRéalisation :\\n\\uf0b7\\nCollecte de besoin métier.\\n\\uf0b7\\nRédaction de spéc...</code> |
  • —Loss: <code>CosineSimilarityLoss</code> with these parameters:
json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }

Evaluation Dataset

matchingrhval10
  • —Dataset: matching_rh_val10 at 16fd0da
  • —Size: 17,380 evaluation samples
  • —Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code>
  • —Approximate statistics based on the first 1000 samples: | | label | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | type | float | string | string | | details | <ul><li>min: 0.0</li><li>mean: 0.84</li><li>max: 1.0</li></ul> | <ul><li>min: 80 tokens</li><li>mean: 352.97 tokens</li><li>max: 3661 tokens</li></ul> | <ul><li>min: 90 tokens</li><li>mean: 615.01 tokens</li><li>max: 6579 tokens</li></ul> |
  • —Samples: | label | sentence1 | sentence2 | |:-----------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>1.0</code> | <code>{"type": "opportunity", "customercode": "", "opportunitytitle": "DATA MANAGER - La POSTE", "opportunityplace": "", "opportunityexpertisearea": "Services", "opportunitytools": "", "opportunityactivityarea": "", "opportunitytype": "1", "opportunitydescription": "", "opportunitycriteria": "", "opportunityextract": 1}</code> | <code>{"type": "candidate", "customercode": "", "title": "Senior Consultant/Project Manager - Data Management", "skills": "", "education": "", "experience": "-1", "tools": "", "languages": "", "mobility": "", "expertisearea": "", "activityarea": "", "listdiplomes": "BACHELOR - Mathématiques Appliquées - stratégique Université Paris I Panthéon Sorbonne, DEUG - Option Statistique - stratégique Université Paris I Panthéon Sorbonne", "typeOf": "-1", "source": "1", "informationComments": "adresse perso consultant : 99 rue Alfred DININ 92000 Nanterre", "extract": 1, "experiences": "[{'skills': '', 'startMonth': '', 'endDate': '', 'startYear': '', 'description': \"Avril ❖Mission : Automatisation et fiabilisation des calculs de l'inventaire de réassurance sur les produits de prévoyance individuelle commercialisés par les partenaires d'Axa France (SAS/SQL) Etude de l'efficience et de la rentabilité des traités de réassurance mis en place pour sécuriser le portefeuille de ces produits (SAS/C++...</code> | | <code>1.0</code> | <code>{"type": "opportunity", "customercode": "", "opportunitytitle": "BABILOU - Responsable infra", "opportunityplace": "", "opportunityexpertisearea": "Autres", "opportunitytools": "", "opportunityactivityarea": "", "opportunitytype": "1", "opportunitydescription": "", "opportunitycriteria": "", "opportunityextract": 1}</code> | <code>{"type": "candidate", "customercode": "", "title": "CHEF DE PROJET INFRASTRUCTURE", "skills": "", "education": "", "experience": "-1", "tools": "", "languages": "", "mobility": "", "expertisearea": "", "activityarea": "", "listdiplomes": "2020 - Microsoft Azure Artificial Intelligence - Microsoft Azure Fundamentals, 2014 - DEA - Probabilités et Applications - Université, 2003 - Diplôme d'ingénieur - Télécoms ENST ParisTech, 2003 - DEA - Signal et Communications Numériques - Université de Nice Sophia-Antipolis", "typeOf": "-1", "source": "1", "informationComments": "", "extract": 1, "experiences": "[{'skills': '', 'startMonth': '', 'endDate': '', 'startYear': '', 'description': '23 mois Études, architecture, ingénierie et paramétrage des réseaux de signalisation et de transit', 'company': '', 'location': '', 'id': '1947', 'title': 'Ingénieur accès fixe et mobile - Contexte - 01/10/2005 - 01/08/2007', 'endMonth': '', 'endYear': '', 'startDate': ''}, {'skills': '', 'startMonth': '', '...</code> | | <code>1.0</code> | <code>{"type": "opportunity", "customercode": "", "opportunitytitle": "DGFIP - ONEPOINT - Consultant JCL", "opportunityplace": "", "opportunityexpertisearea": "Autres", "opportunitytools": "", "opportunityactivityarea": "", "opportunitytype": "1", "opportunitydescription": "", "opportunitycriteria": "", "opportunityextract": 1}</code> | <code>{"type": "candidate", "customercode": "", "title": "analyste developpeur pacbase cobol db2", "skills": "cobol, pacbase, db2, cics", "education": "", "experience": "-1", "tools": "", "languages": "", "mobility": "mondeeuropefranceiledefranceparis, mondeeuropefranceiledefranceseineetmarne, mondeeuropefranceiledefranceyvelines, mondeeuropefranceiledefranceessone, mondeeuropefranceiledefrancehautsdeseine92, mondeeuropefranceiledefranceseinesaintdenis, mondeeuropefranceiledefrancevaldemarne, mondeeuropefranceiledefrancevaloise", "expertisearea": "", "activityarea": "", "listdiplomes": "", "typeOf": "0", "source": "", "informationComments": "Sabrina Kadrie\n06 83 65 01 64\nsabrina20@orange.fr", "extract": 1, "experiences": "[]"}</code> |
  • —Loss: <code>CosineSimilarityLoss</code> with these parameters:
json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }

Training Hyperparameters

Non-Default Hyperparameters
  • —eval_strategy: steps
  • —per_device_train_batch_size: 4
  • —per_device_eval_batch_size: 4
  • —learning_rate: 2e-05
  • —num_train_epochs: 1
  • —warmup_ratio: 0.1
  • —log_level: error
  • —log_level_replica: passive
  • —log_on_each_node: False
  • —logging_nan_inf_filter: False
  • —bf16: True
All Hyperparameters

<details><summary>Click to expand</summary>

  • —overwrite_output_dir: False
  • —do_predict: False
  • —eval_strategy: steps
  • —prediction_loss_only: True
  • —per_device_train_batch_size: 4
  • —per_device_eval_batch_size: 4
  • —per_gpu_train_batch_size: None
  • —per_gpu_eval_batch_size: None
  • —gradient_accumulation_steps: 1
  • —eval_accumulation_steps: None
  • —torch_empty_cache_steps: None
  • —learning_rate: 2e-05
  • —weight_decay: 0.0
  • —adam_beta1: 0.9
  • —adam_beta2: 0.999
  • —adam_epsilon: 1e-08
  • —max_grad_norm: 1.0
  • —num_train_epochs: 1
  • —max_steps: -1
  • —lr_scheduler_type: linear
  • —lr_scheduler_kwargs: {}
  • —warmup_ratio: 0.1
  • —warmup_steps: 0
  • —log_level: error
  • —log_level_replica: passive
  • —log_on_each_node: False
  • —logging_nan_inf_filter: False
  • —save_safetensors: True
  • —save_on_each_node: False
  • —save_only_model: False
  • —restore_callback_states_from_checkpoint: False
  • —no_cuda: False
  • —use_cpu: False
  • —use_mps_device: False
  • —seed: 42
  • —data_seed: None
  • —jit_mode_eval: False
  • —use_ipex: False
  • —bf16: True
  • —fp16: False
  • —fp16_opt_level: O1
  • —half_precision_backend: auto
  • —bf16_full_eval: False
  • —fp16_full_eval: False
  • —tf32: None
  • —local_rank: 0
  • —ddp_backend: None
  • —tpu_num_cores: None
  • —tpu_metrics_debug: False
  • —debug: []
  • —dataloader_drop_last: False
  • —dataloader_num_workers: 0
  • —dataloader_prefetch_factor: None
  • —past_index: -1
  • —disable_tqdm: False
  • —remove_unused_columns: True
  • —label_names: None
  • —load_best_model_at_end: False
  • —ignore_data_skip: False
  • —fsdp: []
  • —fsdp_min_num_params: 0
  • —fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
  • —fsdp_transformer_layer_cls_to_wrap: None
  • —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
  • —parallelism_config: None
  • —deepspeed: None
  • —label_smoothing_factor: 0.0
  • —optim: adamwtorchfused
  • —optim_args: None
  • —adafactor: False
  • —group_by_length: False
  • —length_column_name: length
  • —ddp_find_unused_parameters: None
  • —ddp_bucket_cap_mb: None
  • —ddp_broadcast_buffers: False
  • —dataloader_pin_memory: True
  • —dataloader_persistent_workers: False
  • —skip_memory_metrics: True
  • —use_legacy_prediction_loop: False
  • —push_to_hub: False
  • —resume_from_checkpoint: None
  • —hub_model_id: None
  • —hub_strategy: every_save
  • —hub_private_repo: None
  • —hub_always_push: False
  • —hub_revision: None
  • —gradient_checkpointing: False
  • —gradient_checkpointing_kwargs: None
  • —include_inputs_for_metrics: False
  • —include_for_metrics: []
  • —eval_do_concat_batches: True
  • —fp16_backend: auto
  • —push_to_hub_model_id: None
  • —push_to_hub_organization: None
  • —mp_parameters:
  • —auto_find_batch_size: False
  • —full_determinism: False
  • —torchdynamo: None
  • —ray_scope: last
  • —ddp_timeout: 1800
  • —torch_compile: False
  • —torch_compile_backend: None
  • —torch_compile_mode: None
  • —include_tokens_per_second: False
  • —include_num_input_tokens_seen: False
  • —neftune_noise_alpha: None
  • —optim_target_modules: None
  • —batch_eval_metrics: False
  • —eval_on_start: False
  • —use_liger_kernel: False
  • —liger_kernel_config: None
  • —eval_use_gather_object: False
  • —average_tokens_across_devices: False
  • —prompts: None
  • —batch_sampler: batch_sampler
  • —multi_dataset_batch_sampler: proportional
  • —router_mapping: {}
  • —learning_rate_mapping: {}

</details>

Training Logs

<details><summary>Click to expand</summary>

EpochStepTraining LossValidation Loss
0.00675000.2078-
0.013410000.1805-
0.020215000.1644-
0.026920000.1455-
0.033625000.1326-
0.040330000.1320.1514
0.047135000.1292-
0.053840000.1199-
0.060545000.1223-
0.067250000.1219-
0.074055000.1116-
0.080760000.11490.1483
0.087465000.1149-
0.094170000.1243-
0.100975000.1204-
0.107680000.1116-
0.114385000.109-
0.121090000.1110.1289
0.127895000.1168-
0.1345100000.1121-
0.1412105000.1054-
0.1479110000.1031-
0.1547115000.0994-
0.1614120000.09680.1204
0.1681125000.0932-
0.1748130000.0978-
0.1816135000.0996-
0.1883140000.0974-
0.1950145000.095-
0.2017150000.09260.1139
0.2085155000.0928-
0.2152160000.1007-
0.2219165000.0933-
0.2286170000.0903-
0.2354175000.0912-
0.2421180000.09270.1124
0.2488185000.0927-
0.2555190000.1001-
0.2623195000.0951-
0.2690200000.0893-
0.2757205000.0874-
0.2824210000.08540.1100
0.2892215000.0905-
0.2959220000.0858-
0.3026225000.0906-
0.3093230000.0899-
0.3161235000.0861-
0.3228240000.09340.1063
0.3295245000.0995-
0.3362250000.0905-
0.3430255000.0875-
0.3497260000.074-
0.3564265000.0875-
0.3631270000.08210.1043
0.3699275000.0877-
0.3766280000.0837-
0.3833285000.0854-
0.3900290000.0754-
0.3968295000.0803-
0.4035300000.08720.1029
0.4102305000.0829-
0.4169310000.0841-
0.4237315000.0861-
0.4304320000.0827-
0.4371325000.0867-
0.4438330000.08080.1028
0.4506335000.081-
0.4573340000.0789-
0.4640345000.0774-
0.4707350000.084-
0.4775355000.0866-
0.4842360000.08390.1010
0.4909365000.0849-
0.4976370000.0834-
0.5044375000.0832-
0.5111380000.0739-
0.5178385000.077-
0.5245390000.07990.1016
0.5313395000.0775-
0.5380400000.0788-
0.5447405000.0821-
0.5514410000.0796-
0.5582415000.0795-
0.5649420000.08360.0976
0.5716425000.0783-
0.5783430000.082-
0.5851435000.0788-
0.5918440000.0849-
0.5985445000.0754-
0.6052450000.07640.0989
0.6120455000.0736-
0.6187460000.0805-
0.6254465000.0788-
0.6321470000.0724-
0.6389475000.0833-
0.6456480000.07520.0972
0.6523485000.0733-
0.6590490000.0686-
0.6658495000.0802-
0.6725500000.0817-
0.6792505000.0772-
0.6859510000.07460.0958
0.6927515000.0742-
0.6994520000.0732-
0.7061525000.0711-
0.7128530000.0773-
0.7196535000.0782-
0.7263540000.07740.0953
0.7330545000.0788-
0.7397550000.0667-
0.7465555000.0721-
0.7532560000.074-
0.7599565000.0698-
0.7666570000.07030.0948
0.7734575000.0718-
0.7801580000.0764-
0.7868585000.078-
0.7935590000.0784-
0.8003595000.0771-
0.8070600000.07660.0937
0.8137605000.0758-
0.8204610000.0747-
0.8272615000.0814-
0.8339620000.0719-
0.8406625000.067-
0.8473630000.07170.0937
0.8541635000.0732-
0.8608640000.0755-
0.8675645000.0749-
0.8742650000.072-
0.8810655000.071-
0.8877660000.07020.0923
0.8944665000.0676-
0.9011670000.0753-
0.9079675000.0734-
0.9146680000.0654-
0.9213685000.073-
0.9280690000.07030.0922
0.9348695000.07-
0.9415700000.0716-
0.9482705000.0811-
0.9549710000.0722-
0.9617715000.0697-
0.9684720000.07460.0915
0.9751725000.0768-
0.9818730000.0691-
0.9886735000.0718-
0.9953740000.0707-

</details>

Framework Versions

  • —Python: 3.10.16
  • —Sentence Transformers: 5.1.1
  • —Transformers: 4.56.2
  • —PyTorch: 2.8.0+cu128
  • —Accelerate: 1.10.1
  • —Datasets: 4.1.1
  • —Tokenizers: 0.22.1

Citation

BibTeX

Sentence Transformers
bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

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